How to use flexibility for accelerating the green transition

Transcription

1 How to use flexibility for accelerating the green transition Henrik Madsen DTU Compute, Lyngby, Denmark NTNU, FME-ZEN, Trondheim, Norway

2 The Danish Wind Power Case... balancing of the power system In 2008 windwind power did cover the entire In 2008 power did cover the entire demand of demand ofelectricity electricityinin hours hours(west DK) (West DK) In 2017 more than 44 pct of electricity load was covered by wind power. For several days the wind power production was more than 100 pct of the power load. July 10th, 2015 more than 140 pct of the power load was covered by wind power

3 Challenges A lm os tn o Fl ex ib i lit y

4 Ideas / Solutions

5 Temporal and Spatial Scales The Smart-Energy Operating-System (SE-OS) is used to develop, implement and test of solutions (layers: data, models, optimization, control, communication) for operating flexible electrical energy systems at all scales.

6 Flexibility enabled using data intelligence (AI)

7 Smart-Energy OS

8 Control and Optimization Day Ahead: Stoch. Programming based on eg. Scenarios Cost: Related to the market (one or two levels) Direct Control: Actuator: Power Two-way communication Models for DERs are needed Constraints for the DERs (calls for state est.) Contracts are complicated Indirect Control: Actuator: Price Cost: E-MPC at low (DER) level, One-way communication Models for DERs are not needed Simple 'contracts' In Wiley Book: Control of Electric Loads in Future Electric Energy Systems, 2015

9 AI enabled Flexible Energy Systems Automatic and self-cal. methods based on Big Data and AI Labs Virtual, HiL, Live Nested sequence of systems systems of systems Hierarchy of stoch. optimization and control problems Bidding clearing activation at higher levels Multivariate probabilistic forecasting Cloud or Fog (IoT, IoS) based solutions eg. for control Facilitates energy systems integration (power, gas, thermal,...) Allow for new players (specialized aggregators) Simple setup for the communication and contracts Harvest flexibility at all levels

10 Case study Control of heat pumps for swimming pools (Minimization of CO2 emission)

11 Partner s

12 Example: CO2-based control

13 For more information... See for instance contact Henrik Madsen (DTU Compute / NTNU-ZEN) hmad@dtu.dk henrik.madsen@ntnu.no Acknowledgement: Innovation Fund Denmark Research Council of Norway (FME-ZEN)

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15 Some case studies.

16 SE-OS Control loop design logical drawing Termostat actuator Data Sensors 16

17 Lab testing.

18 SN-10 Smart House Prototype

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20 Source: pro.electicitymap.org

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22 Example: Price-based control 22

23 Penalty Function (examples)

24 Case study No. 2 Wastewater Treatment Plants

25 Kolding WWTP

26 Energy Flexibility in Wastewater Treatment

27 Sewer System Annual Elspot Savings

28 Some 'randomly picked' books on modeling...

29 Some references Madsen, Henrik; Holst, Jan. Estimation of Continuous-Time Models for the Heat Dynamics of a Building. In: Energy and Buildings, Vol. 22, 1995 Andersen, Klaus Kaae; Madsen, Henrik; Hansen, Lars Henrik. Modelling the heat dynamics of a building using stochastic differential equations. In: Energy and Buildings, Vol. 31, No. 1, 2000, p Kristensen, Niels Rode; Madsen, Henrik; Jørgensen, Sten Bay. Using continuous time stochstic modelling and nonparametric statistics to improve the quality of first principles models. In: Computer Aided Chemical Engineering, Vol. 10, 2002, p Kristensen, N.R.; Madsen, Henrik; Jørgensen, Sten Bay. A unified framefork for systematic model improvement. In: Process Systems Engineering, Vol. 15, 2003, p Kristensen, Niels Rode; Madsen, Henrik; Jørgensen, Sten Bay. Parameter Estimation in Stochastic Grey-Box Models. In: Automatica, Vol. 40, No. 2, 2004, p Nielsen, Henrik Aalborg; Madsen, Henrik. Modelling the Heat Consumption in District Heating Systems using a Grey-box approach. In: Energy and Buildings, Vol. 38, No. 1, 2006, p Friling, N.; Jimenez, M.J.; Bloem, H.; Madsen, Henrik. Modelling the heat dynamics of building integrated and ventilated photovoltaic modules. In: Energy and Buildings, Vol. 41, No. 10, 2009, p Bacher, Peder; Madsen, Henrik. Identifying suitable models for the heat dynamics of buildings. In: Energy and Buildings, Vol. 43, No. 7, 2011, p Lodi, C.; Bacher, Peder; Cipriano, J.; Madsen, Henrik. Modelling the heat dynamics of a monitored Test Reference Environment for Building Integrated Photovoltaic systems using stochastic differential equations. In: Energy and Buildings, Vol. 50, 2012, p Morales González, Juan Miguel; Pinson, Pierre; Madsen, Henrik. A Transmission-Cost-Based Model to Estimate the Amount of Market-Integrable Wind Resources. In: IEEE Transactions on Power Systems, Vol. 27, No. 2, 2012, p Halvgaard, Rasmus; Bacher, Peder; Perers, Bengt; Andersen, Elsa; Furbo, Simon; Jørgensen, John Bagterp; Poulsen, Niels Kjølstad; Madsen, Henrik. Model predictive control for a smart solar tank based on weather and consumption forecasts. In: Energy Procedia, Vol. 30, 2012, p

30 Some references (cont.) Morales González, Juan Miguel; Pinson, Pierre; Madsen, Henrik. A Transmission-Cost-Based Model to Estimate the Amount of Market-Integrable Wind Resources. In: IEEE Transactions on Power Systems, Vol. 27, No. 2, 2012, p Dorini, Gianluca Fabio ; Pinson, Pierre; Madsen, Henrik. Chance-constrained optimization of demand response to price signals. In: IEEE Transactions on Smart Grid, Vol. 4, No. 4, 2013, p Bacher, Peder; Madsen, Henrik; Nielsen, Henrik Aalborg; Perers, Bengt. Short-term heat load forecasting for single family houses. In: Energy and Buildings, Vol. 65, 2013, p Corradi, Olivier; Ochsenfeld, Henning Peter; Madsen, Henrik; Pinson, Pierre. Controlling Electricity Consumption by Forecasting its Response to Varying Prices. In: IEEE Transactions on Power Systems, Vol. 28, No. 1, 2013, p Zugno, Marco; Morales González, Juan Miguel; Pinson, Pierre; Madsen, Henrik. A bilevel model for electricity retailers participation in a demand response market environment. In: Energy Economics, Vol. 36, 2013, p Meibom, Peter; Hilger, Klaus Baggesen; Madsen, Henrik; Vinther, Dorthe. Energy Comes Together in Denmark: The Key to a Future Fossil-Free Danish Power System. In: IEEE Power & Energy Magazine, Vol. 11, No. 5, 2013, p Andersen, Philip Hvidthøft Delff ; Jiménez, María José ; Madsen, Henrik ; Rode, Carsten. Characterization of heat dynamics of an arctic low-energy house with floor heating. In: Building Simulation, Vol. 7, No. 6, 2014, p Andersen, Philip Hvidthøft Delff; Iversen, Anne; Madsen, Henrik; Rode, Carsten. Dynamic modeling of presence of occupants using inhomogeneous Markov chains. In: Energy and Buildings, Vol. 69, 2014, p Madsen, H, Parvizi, J, Halvgaard, RF, Sokoler, LE, Jørgensen, JB, Hansen, LH & Hilger, KB 2015, 'Control of Electricity Loads in Future Electric Energy Systems'. in AJ Conejo, E Dahlquist & J Yan (eds), Handbook of Clean Energy Systems: Intelligent Energy Systems. vol. 4, Wiley. Halvgaard, RF, Vandenberghe, L, Poulsen, NK, Madsen, H & Jørgensen, JB 2016, Distributed Model Predictive Control for Smart Energy Systems IEEE Transactions on Smart Grid, vol 7, no. 3, pp Bacher, P, de Saint-Aubain, PA, Christiansen, LE & Madsen, H 2016, Non-parametric method for separating domestic hot water heating spikes and space heating Energy and Buildings, vol 130, pp